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Stochastic dominance is a crucial tool for the analysis of choice under risk. It is typically analyzed as a property of two gambles that are taken in isolation. We study how additional independent sources of risk (e.g. uninsurable labor…

Probability · Mathematics 2020-05-14 Luciano Pomatto , Philipp Strack , Omer Tamuz

Given two random variables taking values in a bounded interval, we study whether one dominates the other in higher-order stochastic dominance depends on the reference interval in the model setting. We obtain two results. First, the…

Probability · Mathematics 2025-03-07 Ruodu Wang , Qinyu Wu

We provide a new characterization of second-order stochastic dominance, also known as increasing concave order. The result has an intuitive interpretation that adding a risk with negative expected value in adverse scenarios makes the…

Risk Management · Quantitative Finance 2024-09-30 Yuanying Guan , Muqiao Huang , Ruodu Wang

Higher order risk measures are stochastic optimization problems by design, and for this reason they enjoy valuable properties in optimization under uncertainties. They nicely integrate with stochastic optimization problems, as has been…

Risk Management · Quantitative Finance 2024-02-26 Alois Pichler

Most of the stochastic orders for comparing random variables, considered in the literature, are afflicted with two main drawbacks: (i) lack of connex property and (ii) lack of consideration of any dependence structure between the random…

Methodology · Statistics 2021-03-03 Sugata Ghosh , Asok K. Nanda

In this manuscript, we study stochastic comparisons of the second-order statistics from dependent or independent observations with modified proportional hazard rates models. First, we establish the usual stochastic order of the second-order…

Statistics Theory · Mathematics 2022-01-06 Niu Jiale

Recent studies have analyzed whether one forecast method dominates another under a class of consistent scoring functions. While the existing literature focuses on empirical tests of forecast dominance, little is known about the theoretical…

Methodology · Statistics 2019-12-19 Fabian Krüger , Johanna F. Ziegel

Stochastic dominance serves as a general framework for modeling a broad spectrum of decision preferences under uncertainty, with risk aversion as one notable example, as it naturally captures the intrinsic structure of the underlying…

Machine Learning · Computer Science 2026-01-06 Shicong Cen , Jincheng Mei , Hanjun Dai , Dale Schuurmans , Yuejie Chi , Bo Dai

We provide conditions for the stochastic dominance comparisons of a risk $X$ and an associated risk $X+Z$, where $Z$ represents the uncertainty due to the environment and where $X$ and $Z$ can be dependent. The comparisons depend on both…

Statistics Theory · Mathematics 2025-03-10 Jorge Navarro , José M. Zapata

Convex combinations of i.i.d. random variables without a finite mean can behave in a strikingly different way from the finite-mean case: as the weight vector becomes more balanced, the resulting combination may become stochastically larger,…

Methodology · Statistics 2026-03-10 Tommaso Lando , Paulo Eduardo Oliveira

This contribution examines optimization problems that involve stochastic dominance constraints. These problems have uncountably many constraints. We develop methods to solve the optimization problem by reducing the constraints to a finite…

Optimization and Control · Mathematics 2025-02-27 Rajmadan Lakshmanan , Alois Pichler , Miloš Kopa

The concept of "stochastic precedence" between two real-valued random variables has often emerged in different applied frameworks. In this paper we consider a slightly more general, and completely natural, concept of stochastic precedence…

Probability · Mathematics 2015-06-17 Emilio De Santis , Fabio Fantozzi , Fabio Spizzichino

We propose some new results on the comparison of the minimum or maximum order statistic from a random number of non-identical random variables. Under the non-identical set-up, with certain conditions, we prove that random minimum (maximum)…

Statistics Theory · Mathematics 2024-03-08 Amarjit Kundu , Shovan Chowdhury , Bidhan Modok

In recent years, stochastic dominance for independent and identically distributed (iid) infinite-mean random variables has received considerable attention. The literature has identified several classes of distributions of nonnegative random…

Probability · Mathematics 2026-04-28 Keyi Zeng , Zhenfeng Zou , Yuting Su , Taizhong Hu

Consider two batches of independent or interdependent exponentiated location-scale distributed heterogeneous random variables. This article investigates ordering results for the second-order statistics from these batches when a vector of…

Statistics Theory · Mathematics 2021-04-20 Sangita Das , Suchandan Kayal

The second-largest order statistic is of special importance in reliability theory since it represents the time to failure of a $2$-out-of-$n$ system. Consider two $2$-out-of-$n$ systems with heterogeneous random lifetimes. The lifetimes are…

Statistics Theory · Mathematics 2021-04-20 Sangita Das , Suchandan Kayal

The expectation is an example of a descriptive statistic that is monotone with respect to stochastic dominance, and additive for sums of independent random variables. We provide a complete characterization of such statistics, and explore a…

Theoretical Economics · Economics 2024-08-06 Xiaosheng Mu , Luciano Pomatto , Philipp Strack , Omer Tamuz

This manuscript investigates the stochastic comparisons of the second-order statistics from dependent and heterogeneous general semi-parametric family of distributions observations. Some sufficient conditions on the usual stochastic order…

Statistics Theory · Mathematics 2024-07-29 Guoqiang Lv

In this paper we provide a novel family of stochastic orders that generalizes second order stochastic dominance, which we call the $\alpha,[a,b]$-concave stochastic orders. These stochastic orders are generated by a novel set of "very"…

Probability · Mathematics 2021-04-28 Bar Light , Andres Perlroth

Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning. Nevertheless, it is still understood as an open question how to…

Machine Learning · Statistics 2024-03-05 Christoph Jansen , Georg Schollmeyer , Hannah Blocher , Julian Rodemann , Thomas Augustin
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